Energy Trading Scheme Research Articles

Design of a privacy-preserving decentralized energy trading scheme in blockchain network environment

Small and medium-sized users with distributed generators or flexible demand response resources desire to participate in market transactions solely, rather than aggregated by aggregators. The commonly used decentralized bookkeeping mode, blockchain-based trading schemes can now realize uniform clearing of decentralized transactions with the help of smart contracts. However, there are still risks of excessive disclosure of information and privacy leakage caused by incomplete decentralization. To solve this problem, we propose a decentralized optimization-based market clearing model, which can be applied on blockchain. The model has three advantages: ① limited access to the consumers’ or generators’ private data, ② convenient calculation methods that can be deployed on the smart contract and ③ pure decentralization to realize transparent on-chain market clearing. Moreover, considering the insufficient throughput of the existing blockchain, a blockchain network, which can process the quotation calculation in parallel with various sub-chains, is also designed to support the proposed market clearing model. Finally, the effectiveness of proposed models is validated and tested on the Ethereum private blockchain.

Achieving efficient and Privacy-preserving energy trading based on blockchain and ABE in smart grid

With the advent of the Industry 4.0 era, the development of smart cities based on the Internet of Things (IoT) has reached a new level. As a key component of the Internet of Things (IoT), the security of wireless sensor networks (WSN) has received widespread attention. Among them, Energy Internet, as an important part to support the construction of smart cities, its security and reliability research is becoming more and more important. In the Energy Internet, distributed energy transaction model is a promising approach to replace the traditional centralized transaction model and has become the leading direction of development in energy trading. As the underlying support, blockchain technology is attracting more and more attention due to its advantages, i.e., integrity and non-repudiation. However, most blockchain-based trading models face the problem of privacy disclosure. In this paper, to solve this problem, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is introduced as the core algorithm to reconstruct the transaction model. Specifically, we build a general model for distributed transactions called PP-BCETS (Privacy-preserving Blockchain Energy Trading Scheme). It can achieve fine-grained access control through transaction arbitration in the ciphertext form. This design can maximize the protection of privacy information, and considerably improve the security and reliability of the transaction model. Additionally, a credibility-based equity proof consensus mechanism is proposed in PP-BCETS, which can greatly increase the operation efficiency. The security analysis and experimental evaluations are conducted to prove the validity and practicability of our proposed scheme.

Optimal operating strategy of high-temperature heat and power storage system coupled with a wind farm in energy market

Due to the fluctuating nature of solar and wind power plants, they are mostly accompanied by energy storage units to be able to offer dispatchable energy output. One of the main challenges of energy storage units in renewable power plants, however, is determining an efficient and optimal energy trading strategy as the majority of the electricity should be traded in the day-ahead market. As a result, the plants are imposed on huge penalties due to less- or over-supply compared to that bided on the day before. This work presents a novel model to optimize the bidding strategy of a wind farm coupled with a high-temperature heat and power energy storage system (based on different designs) in the energy market. This novel storage technology is highly efficient and offers cogeneration of heat and electricity. The proposed problem determines the best bidding value to maximize the total profit of energy sales from a monthly perspective. The case study includes a real wind farm and Danish energy market. The results reveal the importance of having a precise model of marketing for such plants and the effect of small deviation from the optimal marketing strategy on the net annual revenue of the power plant.

ElectroBlocks: A blockchain‐based energy trading scheme for smart grid systems

SummarySmart grid systems are widely used across the world for providing demand response management between users and service providers. In most of the energy distributions scenarios, the traditional grid systems use the centralized architecture, which results in large transmission losses and high overheads during power generation. Moreover, owing to the presence of intruders or attackers, there may be a mismatch between demand and supply between utility centers (suppliers) and end users. Thus, there is a need for an automated energy exchange to provide secure and reliable energy trading between users and suppliers. We found, from the existing literature, that blockchain can be an effective solution to handle the aforementioned issues. Motivated by these facts, we propose a blockchain‐based smart energy trading scheme, ElectroBlocks, which provides efficient mechanisms for secure energy exchanges between users and service providers. In ElectroBlocks, nodes in the network validate the transaction using two algorithms that are cost aware and store aware. The cost‐aware algorithm locates the nearest node that can supply the energy, whereas the store‐aware algorithm ensures that the energy requests go to the node with the lowest storage space. We evaluated the performance of the ElectroBlocks using performance metrics such as mining delay, network exchanges, and storage energy. The simulation results obtained demonstrate that ElectroBlocks maintains a secure trade‐off between users and service providers when using the proposed cost‐aware and store‐aware algorithms.

A motivational game-theoretic approach for peer-to-peer energy trading in islanded and grid-connected microgrid

Global warming has made the transition to zero-carbon emissions generation a necessity for a healthy and green society. This requires that the dependability on the main grid, which is mostly driven by fossil fuel, natural gas, and coal, be reduced. Peer-to-peer (P2P) energy trading has been proposed to achieve this objective. However, the success of such trading depends on the fulfillment of energy-related objectives of participants. The identification of energy-related objectives of participants and their fulfillment is a challenging task. For this purpose, this paper proposes a game-theoretic approach to motivate prosumers for P2P energy trading in both the islanded and grid-connected modes. First, a model that addresses the basic energy-related objectives of both producers and consumers is proposed along with the layered architecture of P2P market to identify and categorize the existing technologies. Then, an extensive form game based on non-cooperative game theory is described and the existence of its strict Nash equilibrium is evaluated. An energy allocation policy has also been devised that motivates prosumers by ensuring that none of them exits the market without trading irrespective of the balance between supply and demand. The proposed energy trading scheme is evaluated on the IEEE-14 bus system with 8 producers and 11 consumers as market participants. We performed comprehensive experiments and the results of these experiments are compared with the previous studies and found that 33% to 7% reduction in energy bills of the consumers is achieved from the proposed scheme which boldly shows the effectiveness of the proposed scheme.

A Nash-bargaining model for trading of electricity between aggregations of peers

In the last several years, the growth in household solar generation and the lack of success of the feed-in-tariff programs have led to the rise of peer-to-peer (P2P) energy trading schemes among prosumers. However, a change that has started more recently is the growth of smart homes and businesses, of which loads are IoT controlled and are supported by advanced metering infrastructure (AMI). This has created a new opportunity for smart homes and businesses to form aggregations (coalitions) and participate in cooperative load management and energy trading. Unlike energy trading among individual prosumers in most P2P networks, a new trading opportunity that is emerging is between aggregations of peers of smart homes and businesses and electric vehicles (EVs). In this paper, we consider one such trading scenario between two aggregations, of which one has smart homes and businesses with load consuming entities (not prosumers), and the other has EVs only. The aggregation with smart homes and businesses derive cost reduction through optimal load scheduling based on load preferences, market-based pricing of electricity, and opportunity to trade (buy) energy from the aggregation with EVs. Whereas the aggregation of EVs optimally schedules charging to meet EV needs and uses stored energy to trade (sell). A generalized Nash bargaining model is developed for obtaining optimal trading strategies in the form of plain or swing option contracts. A sample numerical problem scenario is used to show that suitable contracts can be derived that allow aggregations of peers to mutually benefit from energy trading.It is shown that there exist numerous alternative optimal solutions to the Nash bargaining problem. The solutions comprise different combinations of strike price and option value, for all of which savings to the parties remain constant. For plain option, with a contract quantity of 1 MWh, the total savings generated is equivalent to the average price of 1.62 MWh of electricity. Interactions among contract parameters (such as strike price, option value, and option quantity) and the relative market power of the aggregations are also examined.

Constructing energy-consuming right trading system for China's manufacturing industry in 2025

In order to effectively curb the rapidly-increasing energy consumption in manufacturing industry, China has clearly stated in Made in China2025, to reduce energy consumption per unit of industrial added value of the manufacturing by 18% in 2020 and 34% in 2025 relative to the 2015 level. Based on the ZSG-DEA and CAT models, this paper attempts to establish an efficiency-oriented energy-consuming right trading system (ECRTS) to design the energy allocation scheme along with the energy trading scheme for China’s 18 manufacturing sectors in 2020 and 2025. We find sectors with the allocated energy-consuming right could not only reach the DEA efficiency frontier, but also upgrade their energy-consuming mix. Further, the allocation scheme considering the sectoral heterogeneity is more rational as it relieves the pressure on some sectors to conserve energy. Finally, by integrating energy offsetting mechanism into the united energy market, the energy trading mechanism designed in this work can largely simplify the transaction process and improve the after-trading efficiency for all target sectors.

Research on daily energy trading strategy of multi-microgrid on distribution side

This paper studies the energy trading problem between multi-micro grids on the distribution side, and proposes a multi-micro grid energy trading method based on Nash bargaining. The Nash bargaining negotiation breakpoint is the optimal operating cost for direct trading between microgrid operators and distribution operators. This paper considers the cost of over-network transmission of electrical energy in the distribution network, and builds a cooperative game model for multi-microgrid bargaining transactions. Game equilibrium solving problem is solved by genetic algorithm.

DePET: A Decentralized Privacy-Preserving Energy Trading Scheme for Vehicular Energy Network via Blockchain and K - Anonymity

With the gradually opening of energy markets and popularization of Electric Vehicles (EVs), EVs can transmit, dispatch and recharge energy in different markets and domains dynamically. However, in Vehicular Energy Network, EVs may randomly enter and leave a market, it imposes a difficult problem in that how to schedule and distribute energy effectively. Additionally, the location of EV owners usually includes sensitive information such as home addresses, company names, hospital traces, and so on, which may be collected by attackers and may result in the privacy leakage about EV owners. In this article, we propose a decentralized blockchain-enabled energy trading scheme that can trade cross over various domains efficiently, which enables reliable transactions between EVs and energy nodes within short processing delay. It can also preserve the privacy of EV owners, by adopting the k-anonymity method in constructing a united request to hide the location information and creating a clocking area based on undirected graphs. Even though the server is maliciously attacked, the attacker cannot distinguish among EV owners, which breaks the linkage between real locations and identities to preserve EV owners' privacy. Finally, we conduct a comprehensive experimental evaluation to evaluate the trading performance and location privacy protection performance. The simulation results show that our proposed architecture outperforms over most state-of-the-art schemes in terms of processing delay and location privacy awareness.

Day-Ahead Energy Trading Strategy of Regional Integrated Energy System Considering Energy Cascade Utilization

The traditional energy system has the disadvantages of high energy consumption and high emission. Through multiple energy networks interconnection and flexible energy conversion, the regional integrated energy system (RIES) can satisfy various energy demands and achieve clean and efficient energy utilization. However, the demand and supply relationship of RIES is complex. Without a reasonable energy supply structure, it’s difficult for RIES to give full play to the economic and efficient operation. At the same time, the multi-energy trading and service mechanisms are still unclear. The promotion of RIES brings challenges and opportunities to energy sector. In order to address the issues in the promotion of RIES, a day-ahead energy trading strategy of RIES considering energy cascade utilization is proposed in this paper. Firstly, the relationship between energy suppliers (ESs), integrated energy service provider (IESP) and energy users (EUs) is clarified. And their benefits are clarified in the energy trading strategy. Furthermore, the day-ahead energy trading model, which is a bi-level optimization model is built. In the upper model, the IESP purchases electric energy and natural gas from ESs at wholesale prices and set energy retail prices. According to energy cascade utilization theory, the IESP make decisions on the energy retail plan and energy scheduling. In the lower model, optimal energy consuming plan are given for EUs. In this model, energy grades of energy production, conversion and storage devices output are classified and the energy grade demands of different energy loads are divided. And the energy cascade utilization trading model is simulated based on actual data. The simulation results show that the trading strategy is feasible and the application of energy cascade utilization theory can further improve the energy utilization efficiency of RIES.

Peer-to-Peer Energy Trading in Virtual Power Plant Based on Blockchain Smart Contracts

A novel Peer-to-peer (P2P) energy trading scheme for a Virtual Power Plant (VPP) is proposed by using Smart Contracts on Ethereum Blockchain Platform. The P2P energy trading is the recent trend the power society is keen to adopt carrying out several trial projects as it eases to generate and share the renewable energy sources in a distributed manner inside local community. Blockchain and smart contracts are the up-and-coming phenomena in the scene of the information technology used to be considered as the cutting-edge research topics in power systems. Earlier works on P2P energy trading including and excluding blockchain technology were focused mainly on the optimization algorithm, Information and Communication Technology, and Internet of Things. Therefore, the financial aspects of P2P trading in a VPP framework is focused and in that regard a P2P energy trading mechanism and bidding platform are developed. The proposed scheme is based on public blockchain network and auction is operated by smart contract addressing both cost and security concerns. The smart contract implementation and execution in a VPP framework including bidding, withdrawal, and control modules developments are the salient feature of this work. The proposed architecture is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Ropsten Test Network.

Hybrid trading scheme for peer‐to‐peer energy trading in transactive energy markets

This study proposes a hybrid energy trading scheme for peer-to-peer (P2P) energy trading in transactive energy markets. Market players can participate in different markets, including local markets, trading with neighbourhood areas, and traditional trading with the grid. In each local market, a community manager (CM) facilitates energy trading and negotiates with other CMs for neighbourhood trading. Based on the heterogeneous preferences of players of each community, each local market has a different price, which is different from market price for neighbourhood trading and trading with the grid. A distributed market clearing mechanism is presented that incorporates coordination among different markets. Also, a network utilisation charge function is defined to apply price signals to the market players to reflect network constraints in energy trading. These price signals are calculated based on the technical constraints in each market and are applied to the corresponding players based on their contribution to network constraints violation. Performance of the proposed trading scheme is evaluated against different market structures and through several case studies.

Optimising the use of ensemble information in numerical weather forecasts of wind power generation

Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility. In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind electricity generation output forecast uncertainty and is extremely valuable at informing wind energy trading strategy.

Reinforcement Learning-Based Microgrid Energy Trading With a Reduced Power Plant Schedule

With dynamic renewable energy generation and power demand, microgrids (MGs) exchange energy with each other to reduce their dependence on power plants. In this article, we present a reinforcement learning (RL)-based MG energy trading scheme to choose the electric energy trading policy according to the predicted future renewable energy generation, the estimated future power demand, and the MG battery level. This scheme designs a deep RL-based energy trading algorithm to address the supply–demand mismatch problem for a smart grid with a large number of MGs without relying on the renewable energy generation and power demand models of other MGs. A performance bound on the MG utility and dependence on the power plant is provided. Simulation results based on a smart grid with three MGs using wind speed data from Hong Kong Observation and electricity prices from ISO New England show that this scheme significantly reduces the average power plant schedule and thus increases the MG utility in comparison with a benchmark methodology.

Consumer Demand for Blockchain-Enabled Peer-to-Peer Electricity Trading in the United Kingdom: An Online Survey Experiment

Peer-to-peer (P2P) energy trading could help address grid management challenges in a decentralizing electricity system, as well as provide other social and environmental benefits. Many existing and proposed trading schemes are enabled by blockchain, a distributed ledger technology (DLT) relying on cryptographic proof of ownership rather than human intermediaries to establish energy transactions. This study used an online survey experiment (n = 2064) to investigate how consumer demand for blockchain-enabled peer-to-peer energy trading schemes in the United Kingdom varies depending on how the consumer proposition is designed and communicated. The analysis provides some evidence of a preference for schemes offering to meet a higher proportion of participants’ energy needs and for those operating at the city/region (as compared to national or neighbourhood) level. People were more likely to say they would participate when the scheme was framed as being run by their local council, followed by an energy supplier, community energy organization, and social media company. Anonymity was the most valued DLT characteristic and mentioning blockchain’s association with Bitcoin led to a substantial decrease in intended uptake. We highlight a range of important questions and implications suggested by these findings for the introduction and operation of P2P trading schemes.

Towards secure and efficient energy trading in IIoT-enabled energy internet: A blockchain approach

Nowadays, with the rapid development and wide application of Internet of Things technology, Industrial Internet of Things (IIoT) has become an essential part of industrial systems. A large number of IoT devices has enabled the diversification and decentralization of energy systems, and the Energy Internet makes it possible to interconnect distributed energy sources and consumers. However, as the number of entities involved in the energy transactions grows and the forms of transactions diversify, the traditional centralized energy trading model faces the problems such as insufficient scalability, vulnerabilities to cyber-attack, and low processing efficiency. To address these challenges, we propose BC-ETS, a secure and efficient BlockChain based Energy Trading Scheme. In BC-ETS, the energy trading model is divided into two levels, which can not only protect privacy but also achieve the balance between power supply and demand. Additionally, to adapt to the relatively weak computing power of the underlying IoT equipment in energy internet, a credibility-based equity proof mechanism is designed to greatly improve the system availability. The analysis shows that BC-ETS can meet the security requirements and has a better performance compared with other similar energy trading schemes.

Grid Influenced Peer-to-Peer Energy Trading

This paper proposes a peer-to-peer (P2P) energy trading scheme that can help a centralized power system to reduce the total electricity demand of its customers at the peak hour. To do so, a cooperative Stackelberg game is formulated, in which the centralized power system acts as the leader that needs to decide on a price at the peak demand period to incentivize prosumers to not seek any energy from it. The prosumers, on the other hand, act as followers and respond to the leader's decision by forming suitable coalitions with neighboring prosumers in order to participate in P2P energy trading to meet their energy demand. The properties of the proposed Stackelberg game are studied. It is shown that the game has a unique and stable Stackelberg equilibrium, as a result of the stability of prosumers' coalitions. At the equilibrium, the leader chooses its strategy using a derived closed-form expression, while the prosumers choose their equilibrium coalition structure. An algorithm is proposed that enables the centralized power system and the prosumers to reach the equilibrium solution. Numerical case studies demonstrate the beneficial properties of the proposed scheme.

Decentralized P2P Energy Trading Under Network Constraints in a Low-Voltage Network

The increasing uptake of distributed energy resources in distribution systems and the rapid advance of technology have established new scenarios in the operation of low-voltage networks. In particular, recent trends in cryptocurrencies and blockchain have led to a proliferation of peer-to-peer (P2P) energy trading schemes, which allow the exchange of energy between the neighbors without any intervention of a conventional intermediary in the transactions. Nevertheless, far too little attention has been paid to the technical constraints of the network under this scenario. A major challenge to implementing P2P energy trading is ensuring network constraints are not violated during the energy exchange. This paper proposes a methodology based on sensitivity analysis to assess the impact of P2P transactions on the network and to guarantee an exchange of energy that does not violate network constraints. The proposed method is tested on a typical U.K. low-voltage network. The results show that our method ensures that energy is exchanged between users under the P2P scheme without violating the network constraints, and that users can still capture the economic benefits of the P2P architecture.

A Decentralized Bilateral Energy Trading System for Peer-to-Peer Electricity Markets

Increase in the deployment of distributed energy resources (DERs) has triggered a new trend to redesign electricity markets as consumer-centric markets relying on peer-to-peer (P2P) approaches. In the P2P markets, players can directly negotiate under bilateral energy trading to match demand and supply. The trading scheme should be designed adequately to incentivise players to participate in the trading process actively. This article proposes a decentralized P2P energy trading scheme for electricity markets with high penetration of DERs. A novel algorithm using primal-dual gradient method is described to clear the market in a fully decentralized manner without interaction of any central entity. Also, to incorporate technical constraints in the energy trading, line flow constraints are modeled in the bilateral energy trading to avoid overloaded or congested lines in the system. This market structure respects market players’ preferences by allowing bilateral energy trading with product differentiation. The performance of the proposed method is evaluated using simulation studies, and it is found that market players can trade energy to maximize their welfare without violating line flow constraints. Also, compared with other similar methods for P2P trading, the proposed approach needs lower data exchange and has a faster convergence.

A novel decision strategy for a bilateral energy contract

Due to the relatively high transportation cost of energy in remote regions, an effective and efficient energy trading market is required. In this paper, first, we extend the idea of a smart home community architecture comprising a small number of smart homes that suit these sorts of regions. Second, we propose an energy trading market at two levels. At the top-level, global generators and global consumers participate in energy trading. At the second level, smart homes in a smart home community participate in the energy market as local generators and consumers. Third, we develop a bilateral energy trading scheme and a novel decision strategy for choosing a bilateral price for both seller and buyer. The developed strategy and energy trading schemes are more efficient than those proposed in traditional work as they require far fewer rounds for negotiations between buyers and sellers to arrive at a mutually appropriate bilateral price. Fourth, we present two distinct cases for bilateral energy trading; single seller single buyer and single seller multiple buyers and show that the latter case has more benefits for both the seller and the buyer. Lastly, we conduct a series of experiments to illustrate the effectiveness of the proposed work.